Identification of failed (fissured) fuel rods in nuclear reactors using neural processing and principal component analysis

Abstract

A possible way to detect failed (fissured) rods, within a nuclear fuel assembly, is sounding the rods with ultrasonic pulses and examining the received echo waveforms. The detection is performed by a multilayer feedforward neural classifier, trained according to the backpropagation algorithm. The classifier achieved a detection efficiency of 93% (for failed rods) with 3% as false-alarm probability. Data compaction through principal component analysis reduced the network's input vector to 1.5% of its original length, with no efficiency loss.